203 186 385 359 563 182 [[Data formats]]\npoly 180 200 3 380 181 555 359 378 [[An_initial_path_towards_statistical_analysis#At_least_one_categorical_variable|At least one categorical variable]]\npoly 584 202 407 378 584 556 762 379 [[An_initial_path_towards_statistical_analysis#Only_continuous_variables|Only continuous variables]]\n</imagemap>\n'''How do I know?'''\n* Check the entry on [[Data formats]] to understand the difference between categorical and numeric (including continuous) variables.\n* Investigate your data using <code>str</code> or <code>summary</code>. ''integer'' and ''numeric'' data is not ''categorical'', while ''factorial'', ''ordinal'' and ''character'' data is ''categorical''.\n\n\n== At least one categorical variable ==\n'''Your dataset does not only contain continuous data.''' Does it only consist least one categorical variable ==\n'''Your dataset does not only contain continuous data.''' Does it only consist of categorical data, or of categorical and continuous data?\n<imagemap>Image:Statistics Flowchart - Data Formats.png|650px|center|\npoly 288 2 151 139 289 271 424 138 [[Data formats]]\npoly 137 148 0 285 138 417 273 284  [[An_initial_path_towards_statistical_analysis#Only_categorical_data:_Chi_Square_Test|Only categorical data: Chi Square Test]]\npoly 436 151 299 288 437 420 572 287 [[An_initial_path_towards_statistical_analysis#Categorical_and_continuous_data|Categorical and continuous data]]\n</imagemap>\n\n'''How do I know?'''\n* Investigate your data using <code>str</code> or <code>summary</code>. ''integer'' and ''numeric'' data is not here.\n\nOrdination are one of the pillars of pattern recognition, and therefore play an important role not only in many disciplines, but also in data science in general. The most fundamental differentiation in which analysis you should choose is rooted in the data format. The difference between continuous data and categorical or nominal data is the most fundamental devision that allows you to choose your analysis pathway. The next consideration you need to review is whether you see the ordination as a string point to inspect the data, or whether you are planning to use it as an endpoint or a discrete goal within your path of analysis. Ordinations re indeed great for skimming through data, yet can also serve as a revelation of results you might not get through other approaches. Other consideration regarding ordinations are related to deeper matters of data formats, especially the question of linearity of continuous variables. This already highlights the main problem of ordination techniques, namely that you need a decent overview in order to choose the most suitable analysis, because only